Road quality based routing

ABSTRACT

Aspects of the disclosed technology provide solutions for performing vehicle routing based on road quality data. In some approaches, a process of the technology can include steps for collecting road quality data using at least one vehicle-mounted sensor, identifying two or more routes to a destination location, receiving historical road quality data associated with the two or more routes to the destination location, and calculating a damage projection associated with each of the two or more routes to the destination location, wherein the damage projection is based on the historical road quality data for at least one of the two or more routes to the destination location.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/589,080, filed on Sep. 30, 2019, entitled, ROAD QUALITY BASEDROUTING, which is hereby expressly incorporated by reference in itsentirety.

TECHNICAL FIELD

The present technology relates to recording road quality information andmore particularly routing for vehicles based upon quality of roads.

BACKGROUND

An autonomous vehicle is a motorized vehicle that can navigate without ahuman driver. An exemplary autonomous vehicle includes a plurality ofsensor systems, such as, but not limited to, a camera sensor system, alidar sensor system, a radar sensor system, amongst others, wherein theautonomous vehicle operates based upon sensor signals output by thesensor systems. Specifically, the sensor signals are provided to aninternal computing system in communication with the plurality of sensorsystems, wherein a processor executes instructions based upon the sensorsignals to control a mechanical system of the autonomous vehicle, suchas a vehicle propulsion system, a braking system, or a steering system.

When a vehicle navigates roads, the vehicle will inevitably encounter avariety of different roads with a variety of different levels of roadquality. Human drivers use their judgment to determine which roads totake based on a wide variety of factors. However, it is challenging forthe autonomous vehicle to determine which roads to take, whileminimizing damage to the autonomous vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

The above-recited and other advantages and features of the presenttechnology will become apparent by reference to specific implementationsillustrated in the appended drawings. A person of ordinary skill in theart will understand that these drawings only show some examples of thepresent technology and would not limit the scope of the presenttechnology to these examples. Furthermore, the skilled artisan willappreciate the principles of the present technology as described andexplained with additional specificity and detail through the use of theaccompanying drawings in which:

FIG. 1 shows an example of a content management system and clientdevices;

FIG. 2 shows an example environment of an autonomous vehicle navigatinga route;

FIG. 3 is a flow diagram that illustrates a process for roadquality-based routing; and

FIG. 4 shows an example of a system for implementing certain aspects ofthe present technology.

DETAILED DESCRIPTION

Various examples of the present technology are discussed in detailbelow. While specific implementations are discussed, it should beunderstood that this is done for illustration purposes only. A personskilled in the relevant art will recognize that other components andconfigurations may be used without parting from the spirit and scope ofthe present technology. In some instances, well-known structures anddevices are shown in block diagram form in order to facilitatedescribing one or more aspects. Further, it is to be understood thatfunctionality that is described as being carried out by certain systemcomponents may be performed by more or fewer components than shown.

In general, a vehicle will traverse roads having a wide variety ofdifferent levels of road quality. Roads having a low road quality maycause damage to the vehicle and may also cause discomfort for passengersof the vehicle.

While human drivers can use their judgment to determine which roads todrive on, it is more challenging for an autonomous vehicle. Furthermore,human drivers may use their judgment to determine how quickly to driveon roads having low road quality to mitigate damage to the vehicle andminimize discomfort for the passengers of the vehicle. Not only is itchallenging for the autonomous vehicle to determine which roads to driveon, but it is also challenging for the autonomous vehicle to navigatethe roads at an efficient pace, especially on roads having low roadquality. In other words, it is difficult for the autonomous vehicle todetermine how quickly to drive through roads having low road quality,while also maintaining acceptable levels of damage to the autonomousvehicle and discomfort for the passenger of the autonomous vehicle.Thus, there is a need for routing vehicles based on road quality.

While the present technology is primarily discussed with respect toautonomous vehicles, it applies equally to any routing application(e.g., vehicle navigation apps, ride share services that provide routesto vehicles in a fleet, etc.) that can take into account road qualitywhen routing vehicles.

FIG. 1 illustrates environment 100 that includes an autonomous vehicle102 in communication with a remote computing system 150.

The autonomous vehicle 102 can navigate about roadways without a humandriver based upon sensor signals output by sensor systems 104-106 of theautonomous vehicle 102. The autonomous vehicle 102 includes a pluralityof sensor systems 104-106 (a first sensor system 104 through an Nthsensor system 106). The sensor systems 104-106 are of different typesand are arranged about the autonomous vehicle 102. For example, thefirst sensor system 104 may be a camera sensor system, and the Nthsensor system 106 may be a lidar sensor system. Other exemplary sensorsystems include radar sensor systems, global positioning system (GPS)sensor systems, inertial measurement units (IMU), infrared sensorsystems, laser sensor systems, sonar sensor systems, and the like. Insome embodiments, the sensor systems 104-106 may include suspensionsensors configured to monitor changes in movement and orientation of theautonomous vehicle, such as changes to roll, pitch, and yaw of theautonomous vehicle. In some embodiments, the sensor systems 104-106 mayinclude weight sensors to determine a weight of an object or person on agiven portion of the autonomous vehicle 102. For example, a weightsensor may be attached to a seat to determine a weight of a passenger.Similarly, a weight sensor may be disposed in the suspension system todetermine a total weight of the autonomous vehicle 102 and the objectsand passengers therein.

The autonomous vehicle 102 further includes several mechanical systemsthat are used to effectuate appropriate motion of the autonomous vehicle102. For instance, the mechanical systems can include but are notlimited to, a vehicle propulsion system 130, a braking system 132, and asteering system 134. The vehicle propulsion system 130 may include anelectric motor, an internal combustion engine, or both. The brakingsystem 132 can include an engine brake, brake pads, actuators, and/orany other suitable componentry that is configured to assist indecelerating the autonomous vehicle 102. The steering system 134includes suitable componentry that is configured to control thedirection of movement of the autonomous vehicle 102 during navigation.

The autonomous vehicle 102 further includes a safety system 136 that caninclude various lights and signal indicators, parking brake, airbags,etc. The autonomous vehicle 102 further includes a cabin system 138 thatcan include cabin temperature control systems, in-cabin entertainmentsystems, etc.

The autonomous vehicle 102 additionally comprises an internal computingsystem 110 that is in communication with the sensor systems 104-106 andthe systems 130, 132, 134, 136, and 138. The internal computing systemincludes at least one processor and at least one memory havingcomputer-executable instructions that are executed by the processor. Thecomputer-executable instructions can make up one or more servicesresponsible for controlling the autonomous vehicle 102, communicatingwith remote computing system 150, receiving inputs from passengers orhuman co-pilots, logging metrics regarding data collected by sensorsystems 104-106 and human co-pilots, etc.

The internal computing system 110 can include a control service 112 thatis configured to control the operation of the vehicle propulsion system130, the braking system 108, the steering system 110, the safety system136, and the cabin system 138. The control service 112 receives sensorsignals from the sensor systems 104-106 as well communicates with otherservices of the internal computing system 110 to effectuate operation ofthe autonomous vehicle 102. In some embodiments, control service 112 maycarry out operations in concert one or more other systems of autonomousvehicle 102.

The internal computing system 110 can also include a constraint service114 to facilitate safe propulsion of the autonomous vehicle 102. Theconstraint service 116 includes instructions for activating a constraintbased on a rule-based restriction upon operation of the autonomousvehicle 102. For example, the constraint may be a restriction uponnavigation that is activated in accordance with protocols configured toavoid occupying the same space as other objects, abide by traffic laws,circumvent avoidance areas, etc. In some embodiments, the constraintservice can be part of the control service 112.

The internal computing system 110 can also include a communicationservice 116. The communication service can include both software andhardware elements for transmitting and receiving signals from/to theremote computing system 150. The communication service 116 is configuredto transmit information wirelessly over a network, for example, throughan antenna array that provides personal cellular (long-term evolution(LTE), 3G, 5G, etc.) communication.

In some embodiments, one or more services of the internal computingsystem 110 are configured to send and receive communications to remotecomputing system 150 for such reasons as reporting data for training andevaluating machine learning algorithms, requesting assistance fromremoting computing system or a human operator via remote computingsystem 150, software service updates, ridesharing pickup and drop offinstructions etc.

The internal computing system 110 can also include a latency service118. The latency service 118 can utilize timestamps on communications toand from the remote computing system 150 to determine if a communicationhas been received from the remote computing system 150 in time to beuseful. For example, when a service of the internal computing system 110requests feedback from remote computing system 150 on a time-sensitiveprocess, the latency service 118 can determine if a response was timelyreceived from remote computing system 150 as information can quicklybecome too stale to be actionable. When the latency service 118determines that a response has not been received within a threshold, thelatency service 118 can enable other systems of autonomous vehicle 102or a passenger to make necessary decisions or to provide the neededfeedback.

The internal computing system 110 can also include a user interfaceservice 120 that can communicate with cabin system 138 in order toprovide information or receive information to a human co-pilot or humanpassenger. In some embodiments, a human co-pilot or human passenger maybe required to evaluate and override a constraint from constraintservice 114, or the human co-pilot or human passenger may wish toprovide an instruction to the autonomous vehicle 102 regardingdestinations, requested routes, or other requested operations.

As described above, the remote computing system 150 is configured tosend/receive a signal from the autonomous vehicle 140 regardingreporting data for training and evaluating machine learning algorithms,requesting assistance from remote computing system 150 or a humanoperator via the remote computing system 150, software service updates,rideshare pickup and drop off instructions, etc.

The remote computing system 150 includes an analysis service 152 that isconfigured to receive data from autonomous vehicle 102 (or a fleet ofautonomous vehicles 102) and analyze the data to train or evaluatemachine learning algorithms for operating the autonomous vehicle 102.For example, the analysis service 152 may receive data from sensorsystems 104-106 of the autonomous vehicle 102 and analyze the data totrain machine learning algorithms to determine potential damageassociated with autonomous vehicles 102 traversing specific roads. Theanalysis service 152 can also perform analysis pertaining to dataassociated with one or more errors or constraints reported by autonomousvehicle 102.

The remote computing system 150 can also include a user interfaceservice 154 configured to present metrics, video, pictures, soundsreported from the autonomous vehicle 102 to an operator of remotecomputing system 150. User interface service 154 can further receiveinput instructions from an operator that can be sent to the autonomousvehicle 102.

The remote computing system 150 can also include an instruction service156 for sending instructions regarding the operation of the autonomousvehicle 102. For example, in response to an output of the analysisservice 152 or user interface service 154, instructions service 156 canprepare instructions to one or more services of the autonomous vehicle102 or a co-pilot or passenger of the autonomous vehicle 102.

The remote computing system 150 can also include a rideshare service 158configured to interact with ridesharing application 170 operating on(potential) passenger computing devices. The rideshare service 158 canreceive requests to be picked up or dropped off from passengerridesharing app 170 and can dispatch autonomous vehicle 102 for thetrip. The rideshare service 158 can also act as an intermediary betweenthe ridesharing app 170 and the autonomous vehicle wherein a passengermight provide instructions to the autonomous vehicle to 102 go around anobstacle, change routes, honk the horn, etc.

As described herein, one aspect of the present technology is thegathering and use of data available from various sources to improve ridequality and a passenger experience. The present disclosure contemplatesthat in some instances, this gathered data may include personalinformation. The present disclosure contemplates that the entitiesinvolved with such personal information respect and value privacypolicies and practices.

FIG. 2 shows an example scenario 200 of the autonomous vehicle 102having a route 202 based on road quality. More specifically, the route202 avoids a road 204 having a segment 206 associated with has poor roadquality. Instead, the autonomous vehicle 102 is instructed to traversethe route 202 along a second road 208 having better road quality. Morespecifically, the route 202 may have a plurality of waypoints 210, towhich the autonomous vehicle 102 is instructed to navigate. Road qualitymay be based upon a wide variety of different factors. For example,roads associated with poor road quality may have bumps, potholes, dips,sharp objects lying thereon, prone to flooding, etc. Roads associatedwith better road quality may be smoother, well-marked (i.e. lanes areeasily detectable), straight, etc.

As passengers ride the autonomous vehicle 102 from a start location to adestination, the passengers may prefer to have the autonomous vehicle102 take the second or smoother road 208, while some passengers mayprefer to take the road 204 having poor road quality if the road 204allows the autonomous vehicle 102 to arrive at a destination morequickly.

FIG. 3 shows a process 300 that may be utilized to determine the route202 based on road quality. For explanatory purposes, the process 300 isdescribed to be implemented by the remote computing system 150; however,it is to be understood that the process 300 may be implemented by a widevariety of different systems. For example, the internal computing system110 of the autonomous vehicle 102 may also implement the process 300.

The process 300 begins at step 305 where the remote computing system 150may receive road quality data from the autonomous vehicle 102. Morespecifically, the autonomous vehicle 102 may utilize the sensor systems104-106 to determine road quality data. In some embodiments, the sensorsystems 104-106 of the autonomous vehicle 102 may be configured todetect movements and an orientation of the autonomous vehicle 102. Forexample, the autonomous vehicle 102 may have a suspension sensor that isconfigured to determine and/or monitor roll, pitch, and/or yaw of theautonomous vehicle 102 while the autonomous vehicle 102 travels along aroad. In some embodiments, the autonomous vehicle 102 may utilize thesensor systems 104-106 such as accelerometers, and gyroscopes todetermine accelerations/decelerations from hitting potholes or roughroad services or detecting changes in orientation of autonomous vehicle102. In some embodiments, the autonomous vehicle 102 may utilize thesensor systems 104-106 as cameras, radars, sonar, LiDAR, etc. todetermine the road quality of the road. For example, the autonomousvehicle 102 may generate LiDAR point cloud data of the surface of theroad, and determine a road quality score based on the LiDAR point clouddata. The road quality score may be based on a statistical deviation ofthe actual surface of the road from a best fit flat or continuouscurvature (i.e. a hypothetical high quality road). In one or moreembodiments, the road quality data may be based on a portion of the roadthe autonomous vehicle 102 predicts the autonomous vehicle 102 willdrive on or otherwise come into contact with. In some embodiments, theautonomous vehicle 102 may be in a fleet of autonomous vehicles and maycommunicate with other autonomous vehicles in the fleet of autonomousvehicles to determine road quality of other roads. For example, theautonomous vehicle 102 may locally store mapping data, as will bediscussed in greater detail, that may indicate another autonomousvehicle had already determined a road quality data for a particularroad.

At step 310, the remote computing system 150 creates a road quality flagfor a segment of a virtual map that corresponds to the segment 206 ofthe road 204 that is associated with the poor road quality data. In someembodiments, the remote computing system 150 may store the road qualityflags, the segments, and the virtual map in a map database. Thus, theremote computing system 150 may have access to historical road qualitydata in the virtual map.

At step 315, the remote computing system 150 determines the route 202for the autonomous vehicle 102, which may be in a fleet of autonomousvehicles. In determining the route 202, the remote computing system 150draws upon a wide variety of different factors. As discussed in furtherdetail below, the route 202 may be based upon the road quality data.

In some embodiments, the remote computing system 150 may determine aprojected quantification of damage to the autonomous vehicle 102 if andwhen the autonomous vehicle 102 traverses the segment 206 of the road204 associated with poor road quality data. The projected quantificationof damage to the autonomous vehicle 102 may be determined using thehistorical road quality data in the virtual map, historical maintenancedata for autonomous vehicles 102 that have driven over the segment 206of the road 204 associated with poor road quality data, a speed at whichthe autonomous vehicle 102 may traverse the segment 206 of the road 204associated with poor road quality data, a total weight of the autonomousvehicle 102 and the objects and passengers within the autonomous vehicle102, etc. In some embodiments, a machine learning model may be trainedusing the historical road quality data, the speed at which theautonomous vehicle 102 may traverse the segment 206 of road 204, and thehistorical maintenance data of the autonomous vehicles 102 to determineand/or calculate the projected quantification of damage to theautonomous vehicle 102.

In some embodiments, the remote computing system 150 may also comparethe projected quantification of damage is compared to an amount of timeneeded to avoid the segment 206 of road 204 that is associated with poorroad quality data. For example, the remote computing system 150 mayquantify the amount of time to determine whether the projectedquantification of damage exceeds the quantification of the amount oftime needed to avoid the segment 206 of road 204. When the projectedquantification of damage exceeds the quantification of the amount oftime needed to avoid the segment 206 of road 204, the remote computingsystem 150 may then determine to generate the route 202 to proceed alongthe second road 208 to avoid the segment 206 of road 204 associated withpoor road quality data.

In some embodiments, the remote computing system 150 may also present anoption to the passenger of the autonomous vehicle 102. Morespecifically, the remote computing system 150 may determine at least tworoutes between the start location and the destination. A first route ofthe at least two routes may be a smoother route that may avoid thesegment 206 of the road 204 associated with poor road quality data. Asecond route of the at least two routes may be a quicker route that mayinclude the segment 206 of the road 204 associated with poor roadquality data. The remote computing system 150 may then present theoption to the passenger of the autonomous vehicle 102 between thesmoother route and the quicker route. In some embodiments, the remotecomputing system 150 may determine the passenger's choice throughpreference data in a profile of the passenger in the ridesharingapplication 170. In other words, the routing may be determined inresponse to the preference of the user. In some embodiments, theinternal computing system of the autonomous vehicle 102 may present theoptions to the passenger and/or determine which option the passengerselects and/or the preferences of the option. Thus, the internalcomputing system 110 may then request the selected route from the remotecomputing system 150 in accordance with the selection or preference ofthe passenger.

At step 320, the remote computing system 150 generates the plurality ofwaypoints 210 along the route 202. The plurality of waypoints 210 arelocations that the autonomous vehicle 102 must advance to in a specifiedorder to arrive at the final destination. The remote computing system150 then sends the plurality of waypoints 210 to the autonomous vehicle102. The internal computing system 110 of the autonomous vehicle 102 maybe configured to receive the plurality of waypoints 210 along the route202. Again, the route 202 may avoid the segment 206 or portion of road204 for which the road quality data indicates a poor road quality forthe segment 206 or portion of road 204.

At step 325, the autonomous vehicle 102 navigates to the plurality ofwaypoints 210 in the specified order. In some embodiments, the sensorsystems 104-106 of the autonomous vehicle 102 may be configured tocontinue receiving additional road quality data and other related data.Similarly, the internal computing system 110 and the communicationservice 116 may be configured to continuously send the additional roadquality data and other related data to the remote computing system 150.

At step 330, the autonomous vehicle 102 arrives at the destination. Insome embodiments, the autonomous vehicle 102 may send data of currentstatuses of the various systems onboard to the remote computing system.For example, the autonomous vehicle 102 may determine a current tirepressure of tires installed on the autonomous vehicle 102 and send theinformation to the remote computing system 150. The remote computingsystem 150 may then determine changes between the current statuses ofthe autonomous vehicle 102 and a past status of the autonomous vehicle102 before the trip. For example, the remote computing system 150 maythen determine that the tires are severely underinflated compared toprior to the trip. In some embodiments, the remote computing system 150may then generate a report or log file to further train various machinelearning algorithms based upon the changes in statuses or damage to theautonomous vehicle 102 and the specific roads the autonomous vehicle 102traversed. For example, when the tires are severely underinflated afterthe trip, the remote computing system 150 may determine that the roadsthat the autonomous vehicle 102 traversed may have poor road quality andaccordingly create road quality flags for the roads.

FIG. 4 shows an example of computing system 400, which can be forexample any computing device making up internal computing system 110,remote computing system 150, (potential) passenger device executingrideshare app 170, or any component thereof in which the components ofthe system are in communication with each other using connection 405.Connection 405 can be a physical connection via a bus, or a directconnection into processor 410, such as in a chipset architecture.Connection 405 can also be a virtual connection, networked connection,or logical connection.

In some embodiments, computing system 400 is a distributed system inwhich the functions described in this disclosure can be distributedwithin a datacenter, multiple data centers, a peer network, etc. In someembodiments, one or more of the described system components representsmany such components each performing some or all of the function forwhich the component is described. In some embodiments, the componentscan be physical or virtual devices.

Example system 400 includes at least one processing unit (CPU orprocessor) 410 and connection 405 that couples various system componentsincluding system memory 415, such as read-only memory (ROM) 420 andrandom access memory (RAM) 425 to processor 410. Computing system 400can include a cache of high-speed memory 412 connected directly with, inclose proximity to, or integrated as part of processor 410.

Processor 410 can include any general purpose processor and a hardwareservice or software service, such as services 432, 434, and 436 storedin storage device 430, configured to control processor 410 as well as aspecial-purpose processor where software instructions are incorporatedinto the actual processor design. Processor 410 may essentially be acompletely self-contained computing system, containing multiple cores orprocessors, a bus, memory controller, cache, etc. A multi-core processormay be symmetric or asymmetric.

To enable user interaction, computing system 400 includes an inputdevice 445, which can represent any number of input mechanisms, such asa microphone for speech, a touch-sensitive screen for gesture orgraphical input, keyboard, mouse, motion input, speech, etc. Computingsystem 400 can also include output device 435, which can be one or moreof a number of output mechanisms known to those of skill in the art. Insome instances, multimodal systems can enable a user to provide multipletypes of input/output to communicate with computing system 400.Computing system 400 can include communications interface 440, which cangenerally govern and manage the user input and system output. There isno restriction on operating on any particular hardware arrangement, andtherefore the basic features here may easily be substituted for improvedhardware or firmware arrangements as they are developed.

Storage device 430 can be a non-volatile memory device and can be a harddisk or other types of computer readable media which can store data thatare accessible by a computer, such as magnetic cassettes, flash memorycards, solid state memory devices, digital versatile disks, cartridges,random access memories (RAMs), read-only memory (ROM), and/or somecombination of these devices.

The storage device 430 can include software services, servers, services,etc., that when the code that defines such software is executed by theprocessor 410, it causes the system to perform a function. In someembodiments, a hardware service that performs a particular function caninclude the software component stored in a computer-readable medium inconnection with the necessary hardware components, such as processor410, connection 405, output device 435, etc., to carry out the function.

For clarity of explanation, in some instances, the present technologymay be presented as including individual functional blocks includingfunctional blocks comprising devices, device components, steps orroutines in a method embodied in software, or combinations of hardwareand software.

Any of the steps, operations, functions, or processes described hereinmay be performed or implemented by a combination of hardware andsoftware services or services, alone or in combination with otherdevices. In some embodiments, a service can be software that resides inmemory of a client device and/or one or more servers of a contentmanagement system and perform one or more functions when a processorexecutes the software associated with the service. In some embodiments,a service is a program or a collection of programs that carry out aspecific function. In some embodiments, a service can be considered aserver. The memory can be a non-transitory computer-readable medium.

In some embodiments, the computer-readable storage devices, mediums, andmemories can include a cable or wireless signal containing a bit streamand the like. However, when mentioned, non-transitory computer-readablestorage media expressly exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implementedusing computer-executable instructions that are stored or otherwiseavailable from computer-readable media. Such instructions can comprise,for example, instructions and data which cause or otherwise configure ageneral purpose computer, special purpose computer, or special purposeprocessing device to perform a certain function or group of functions.Portions of computer resources used can be accessible over a network.The executable computer instructions may be, for example, binaries,intermediate format instructions such as assembly language, firmware, orsource code. Examples of computer-readable media that may be used tostore instructions, information used, and/or information created duringmethods according to described examples include magnetic or opticaldisks, solid-state memory devices, flash memory, USB devices providedwith non-volatile memory, networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprisehardware, firmware and/or software, and can take any of a variety ofform factors. Typical examples of such form factors include servers,laptops, smartphones, small form factor personal computers, personaldigital assistants, and so on. The functionality described herein alsocan be embodied in peripherals or add-in cards. Such functionality canalso be implemented on a circuit board among different chips ordifferent processes executing in a single device, by way of furtherexample.

The instructions, media for conveying such instructions, computingresources for executing them, and other structures for supporting suchcomputing resources are means for providing the functions described inthese disclosures.

Although a variety of examples and other information was used to explainaspects within the scope of the appended claims, no limitation of theclaims should be implied based on particular features or arrangements insuch examples, as one of ordinary skill would be able to use theseexamples to derive a wide variety of implementations. Further andalthough some subject matter may have been described in languagespecific to examples of structural features and/or method steps, it isto be understood that the subject matter defined in the appended claimsis not necessarily limited to these described features or acts. Forexample, such functionality can be distributed differently or performedin components other than those identified herein. Rather, the describedfeatures and steps are disclosed as examples of components of systemsand methods within the scope of the appended claims.

What is claimed is:
 1. An autonomous vehicle comprising: a communicationsystem configured to communicate with a remote computing device; atleast one sensor configured to detect autonomous vehicle movements andorientation; and an internal computing system configured to: identifytwo or more routes to a destination location; receive, via thecommunication system, historical road quality data associated with thetwo or more routes to the destination location; calculate a damageprojection associated with each of the two or more routes to thedestination location, wherein the damage projection is based on thehistorical road quality data for the two or more routes to thedestination location; determine, based on the damage projection, that atleast one of the two or more routes to the destination avoids a roadsegment associated with poor road quality data; and route the autonomousvehicle to the destination via the at least one of the two or moreroutes to the destination that avoids a road segment associated withpoor road quality data.
 2. The autonomous vehicle of claim 1, whereinthe internal computing system is further configured to: automaticallyselect a preferred route to the destination location based at least inpart on the damage projection associated with each of the two or moreroutes.
 3. The autonomous vehicle of claim 1, wherein the internalcomputing system is further configured to: present an option to apassenger of the autonomous vehicle, the option comprising a choicebetween: (1) a faster route to the destination, wherein the faster routeis associated with a shorter distance to the destination and includes atleast one road segment that is associated with poor road quality data,or (2) a slower route to the destination, wherein the slower route isassociated with a longer distance to the destination and avoids the atleast one road segment that is associated with poor road quality data.4. The autonomous vehicle of claim 1, wherein the internal computingsystem is further configured to: collect road quality data using the atleast one sensor; and transmit the road quality data to the remotecomputing device, via the communication system.
 5. The autonomousvehicle of claim 1, wherein the at least one sensor comprises at leastone of: a Light Detection and Ranging (LiDAR) sensor, a radar sensor, oran image sensor.
 6. The autonomous vehicle of claim 1, wherein the atleast one sensor comprises at least one of: an accelerometer, or agyroscopic sensor.
 7. The autonomous vehicle of claim 1, wherein theinternal computing system is further configured to: receive, via thecommunication system, road quality data from at least one vehicle in afleet of vehicles.
 8. The autonomous vehicle of claim 1, wherein theinternal computing system is further configured to: receive, via thecommunication system, road quality data from at least one vehicle in afleet of vehicles.
 9. A method, comprising: collecting road quality datausing at least one sensor, wherein the at least one sensor comprises atleast one Light Detection and Ranging (LiDAR) sensor; identifying two ormore routes to a destination location; receiving, via a communicationsystem, historical road quality data associated with the two or moreroutes to the destination location; calculating a damage projectionassociated with each of the two or more routes to the destinationlocation, wherein the damage projection is based on the historical roadquality data for the two or more routes to the destination location;determining, based on the damage projection, that at least one of thetwo or more routes to the destination avoids a road segment associatedwith poor road quality data; and routing the autonomous vehicle to thedestination via the at least one of the two or more routes to thedestination that avoids a road segment associated with poor road qualitydata.
 10. The method of claim 9, further comprising: automaticallyselecting a preferred route to the destination location based at leastin part on the damage projection associated with each of the two or moreroutes.
 11. The method of claim 9, further comprising: present an optionto a passenger of an autonomous vehicle, the option comprising a choicebetween: (1) a faster route to the destination, wherein the faster routeis associated with a shorter distance to the destination and includes atleast one road segment that is associated with poor road quality data,or (2) a slower route to the destination, wherein the slower route isassociated with a longer distance to the destination and avoids the atleast one road segment that is associated with poor road quality data.12. The method of claim 9, further comprising: collecting road qualitydata using the at least one sensor; and transmitting the road qualitydata to the remote computing device, via the communication system. 13.The method of claim 9, wherein the at least one sensor comprises atleast one of: a radar sensor, or an image sensor.
 14. The method ofclaim 9, wherein the at least one sensor comprises at least one of: anaccelerometer, or a gyroscopic sensor.
 15. A non-transitorycomputer-readable storage medium comprising at least one instruction forcausing a computer or processor to: collect road quality data using atleast one sensor, wherein the at least one sensor comprises at least oneLight Detection and Ranging (LiDAR) sensor; identify two or more routesto a destination location; receive historical road quality dataassociated with the two or more routes to the destination location;calculate a damage projection associated with each of the two or moreroutes to the destination location, wherein the damage projection isbased on the historical road quality data for the two or more routes tothe destination location; determine, based on the damage projection,that at least one of the two or more routes to the destination avoids aroad segment associated with poor road quality data; and route theautonomous vehicle to the destination via the at least one of the two ormore routes to the destination that avoids a road segment associatedwith poor road quality data.
 16. The non-transitory computer-readablestorage medium of claim 15, wherein the at least one instruction isfurther configured to cause the computer or processor to: automaticallyselect a preferred route to the destination location based at least inpart on the damage projection associated with each of the two or moreroutes.
 17. The non-transitory computer-readable storage medium of claim15, wherein the at least one instruction is further configured to causethe computer or processor to: present an option to a passenger of theautonomous vehicle, the option comprising a choice between: (1) a fasterroute to the destination, wherein the faster route is associated with ashorter distance to the destination and includes at least one roadsegment that is associated with poor road quality data, or (2) a slowerroute to the destination, wherein the slower route is associated with alonger distance to the destination and avoids the at least one roadsegment that is associated with poor road quality data.
 18. Thenon-transitory computer-readable storage medium of claim 15, wherein theat least one instruction is further configured to cause the computer orprocessor to: collect road quality data using the at least one sensor;and transmit the road quality data to the remote computing device, viathe communication system.
 19. The non-transitory computer-readablestorage medium of claim 15, wherein the at least one sensor comprises atleast one of: a Light Detection and Ranging (LiDAR) sensor, a radarsensor, or an image sensor.
 20. The non-transitory computer-readablestorage medium of claim 15, wherein the at least one sensor comprises atleast one of: an accelerometer, or a gyroscopic sensor.